diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_anatomy_general_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_anatomy_general_en.md
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+---
+layout: model
+title: Extract Anatomical Entities from Oncology Texts
+author: John Snow Labs
+name: ner_oncology_anatomy_general
+date: 2022-11-24
+tags: [licensed, clinical, en, oncology, anatomy, ner]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts anatomical entities using an unspecific label.
+
+## Predicted Entities
+
+`Anatomical_Site`, `Direction`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_anatomy_general_en_4.2.2_3.0_1669298930681.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_anatomy_general", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_anatomy_general", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:--------|:----------------|
+| left | Direction |
+| breast | Anatomical_Site |
+| lungs | Anatomical_Site |
+| liver | Anatomical_Site |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_anatomy_general|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.3 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+Anatomical_Site 2946 549 638 3584 0.84 0.82 0.83
+ Direction 864 209 120 984 0.81 0.88 0.84
+ macro_avg 3810 758 758 4568 0.82 0.85 0.84
+ micro_avg 3810 758 758 4568 0.83 0.83 0.83
+```
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diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_anatomy_granular_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_anatomy_granular_en.md
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+---
+layout: model
+title: Extract Granular Anatomical Entities from Oncology Texts
+author: John Snow Labs
+name: ner_oncology_anatomy_granular
+date: 2022-11-24
+tags: [licensed, clinical, en, oncology, ner, anatomy]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts mentions of anatomical entities using granular labels.
+
+## Predicted Entities
+
+`Direction`, `Site_Lymph_Node`, `Site_Breast`, `Site_Other_Body_Part`, `Site_Bone`, `Site_Liver`, `Site_Lung`, `Site_Brain`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_anatomy_granular_en_4.2.2_3.0_1669299394344.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_anatomy_granular", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_anatomy_granular", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:--------|:------------|
+| left | Direction |
+| breast | Site_Breast |
+| lungs | Site_Lung |
+| liver | Site_Liver |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_anatomy_granular|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.3 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+ Direction 822 221 162 984 0.79 0.84 0.81
+ Site_Lymph_Node 481 38 70 551 0.93 0.87 0.90
+ Site_Breast 88 14 59 147 0.86 0.60 0.71
+Site_Other_Body_Part 604 184 897 1501 0.77 0.40 0.53
+ Site_Bone 252 74 61 313 0.77 0.81 0.79
+ Site_Liver 178 92 56 234 0.66 0.76 0.71
+ Site_Lung 398 98 161 559 0.80 0.71 0.75
+ Site_Brain 197 44 82 279 0.82 0.71 0.76
+ macro_avg 3020 765 1548 4568 0.80 0.71 0.74
+ micro_avg 3020 765 1548 4568 0.80 0.66 0.71
+```
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diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_biomarker_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_biomarker_en.md
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+---
+layout: model
+title: Extract Biomarkers and their Results
+author: John Snow Labs
+name: ner_oncology_biomarker
+date: 2022-11-24
+tags: [licensed, clinical, en, ner, oncology, biomarker]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts mentions of biomarkers and biomarker results from oncology texts.
+
+## Predicted Entities
+
+`Biomarker_Result`, `Biomarker`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_biomarker_en_4.2.2_3.0_1669299787628.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_biomarker", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_biomarker", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-----------------------------------------|:-----------------|
+| negative | Biomarker_Result |
+| CK7 | Biomarker |
+| synaptophysin | Biomarker |
+| Syn | Biomarker |
+| chromogranin A | Biomarker |
+| CgA | Biomarker |
+| Muc5AC | Biomarker |
+| human epidermal growth factor receptor-2 | Biomarker |
+| HER2 | Biomarker |
+| Muc6 | Biomarker |
+| positive | Biomarker_Result |
+| CK20 | Biomarker |
+| Muc1 | Biomarker |
+| Muc2 | Biomarker |
+| E-cadherin | Biomarker |
+| p53 | Biomarker |
+| Ki-67 index | Biomarker |
+| 87% | Biomarker_Result |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_biomarker|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.3 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+Biomarker_Result 1030 148 415 1445 0.87 0.71 0.79
+ Biomarker 1685 272 279 1964 0.86 0.86 0.86
+ macro_avg 2715 420 694 3409 0.87 0.79 0.82
+ micro_avg 2715 420 694 3409 0.87 0.80 0.83
+```
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diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_demographics_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_demographics_en.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,150 @@
+---
+layout: model
+title: Extract Demographic Entities from Oncology Texts
+author: John Snow Labs
+name: ner_oncology_demographics
+date: 2022-11-24
+tags: [licensed, clinical, en, ner, oncology, demographics]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts demographic information from oncology texts, including age, gender, and smoking status.
+
+## Predicted Entities
+
+`Smoking_Status`, `Age`, `Race_Ethnicity`, `Gender`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_demographics_en_4.2.2_3.0_1669300163954.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_demographics", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The patient is a 40-year-old man with history of heavy smoking."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_demographics", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The patient is a 40-year-old man with history of heavy smoking.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:------------|:---------------|
+| 40-year-old | Age |
+| man | Gender |
+| smoking | Smoking_Status |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_demographics|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.6 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+Smoking_Status 60 19 8 68 0.76 0.88 0.82
+ Age 934 33 15 949 0.97 0.98 0.97
+Race_Ethnicity 57 5 5 62 0.92 0.92 0.92
+ Gender 1248 18 6 1254 0.99 1.00 0.99
+ macro_avg 2299 75 34 2333 0.91 0.95 0.93
+ micro_avg 2299 75 34 2333 0.97 0.99 0.98
+```
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diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_diagnosis_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_diagnosis_en.md
new file mode 100644
index 00000000000000..0181422deb71bd
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_diagnosis_en.md
@@ -0,0 +1,163 @@
+---
+layout: model
+title: Detect Entities Related to Cancer Diagnosis
+author: John Snow Labs
+name: ner_oncology_diagnosis
+date: 2022-11-24
+tags: [licensed, clinical, en, ner, oncology]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts entities related to cancer diagnosis, such as Metastasis, Histological_Type or Invasion.
+
+## Predicted Entities
+
+`Histological_Type`, `Staging`, `Cancer_Score`, `Tumor_Finding`, `Invasion`, `Tumor_Size`, `Adenopathy`, `Performance_Status`, `Pathology_Result`, `Metastasis`, `Cancer_Dx`, `Grade`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_diagnosis_en_4.2.2_3.0_1669300474926.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_diagnosis", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma.
+Last week she was also found to have a lung metastasis."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_diagnosis", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma.
+Last week she was also found to have a lung metastasis.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-------------|:------------------|
+| tumor | Tumor_Finding |
+| adenopathies | Adenopathy |
+| invasive | Histological_Type |
+| ductal | Histological_Type |
+| carcinoma | Cancer_Dx |
+| metastasis | Metastasis |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_diagnosis|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.3 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+ Histological_Type 354 63 99 453 0.85 0.78 0.81
+ Staging 234 27 24 258 0.90 0.91 0.90
+ Cancer_Score 36 15 26 62 0.71 0.58 0.64
+ Tumor_Finding 1121 83 136 1257 0.93 0.89 0.91
+ Invasion 154 27 27 181 0.85 0.85 0.85
+ Tumor_Size 1058 126 71 1129 0.89 0.94 0.91
+ Adenopathy 66 10 30 96 0.87 0.69 0.77
+Performance_Status 116 15 19 135 0.89 0.86 0.87
+ Pathology_Result 852 686 290 1142 0.55 0.75 0.64
+ Metastasis 356 15 14 370 0.96 0.96 0.96
+ Cancer_Dx 1302 88 92 1394 0.94 0.93 0.94
+ Grade 201 23 35 236 0.90 0.85 0.87
+ macro_avg 5850 1178 863 6713 0.85 0.83 0.84
+ micro_avg 5850 1178 863 6713 0.85 0.87 0.86
+```
\ No newline at end of file
diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_en.md
new file mode 100644
index 00000000000000..2577ea98f0f58f
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_en.md
@@ -0,0 +1,219 @@
+---
+layout: model
+title: Detect Oncology-Specific Entities
+author: John Snow Labs
+name: ner_oncology
+date: 2022-11-24
+tags: [licensed, clinical, en, oncology, biomarker, treatment]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts more than 40 oncology-related entities, including therapies, tests and staging.
+
+## Predicted Entities
+
+`Histological_Type`, `Direction`, `Staging`, `Cancer_Score`, `Imaging_Test`, `Cycle_Number`, `Tumor_Finding`, `Site_Lymph_Node`, `Invasion`, `Response_To_Treatment`, `Smoking_Status`, `Tumor_Size`, `Cycle_Count`, `Adenopathy`, `Age`, `Biomarker_Result`, `Unspecific_Therapy`, `Site_Breast`, `Chemotherapy`, `Targeted_Therapy`, `Radiotherapy`, `Performance_Status`, `Pathology_Test`, `Site_Other_Body_Part`, `Cancer_Surgery`, `Line_Of_Therapy`, `Pathology_Result`, `Hormonal_Therapy`, `Site_Bone`, `Biomarker`, `Immunotherapy`, `Cycle_Day`, `Frequency`, `Route`, `Duration`, `Death_Entity`, `Metastasis`, `Site_Liver`, `Cancer_Dx`, `Grade`, `Date`, `Site_Lung`, `Site_Brain`, `Relative_Date`, `Race_Ethnicity`, `Gender`, `Oncogene`, `Dosage`, `Radiation_Dose`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_en_4.2.2_3.0_1669306355829.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
+The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
+The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
+The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
+The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-------------------------------|:----------------------|
+| left | Direction |
+| mastectomy | Cancer_Surgery |
+| axillary lymph node dissection | Cancer_Surgery |
+| left | Direction |
+| breast cancer | Cancer_Dx |
+| twenty years ago | Relative_Date |
+| tumor | Tumor_Finding |
+| positive | Biomarker_Result |
+| ER | Biomarker |
+| PR | Biomarker |
+| radiotherapy | Radiotherapy |
+| breast | Site_Breast |
+| cancer | Cancer_Dx |
+| recurred | Response_To_Treatment |
+| right | Direction |
+| lung | Site_Lung |
+| metastasis | Metastasis |
+| 13 years later | Relative_Date |
+| adriamycin | Chemotherapy |
+| 60 mg/m2 | Dosage |
+| cyclophosphamide | Chemotherapy |
+| 600 mg/m2 | Dosage |
+| six courses | Cycle_Count |
+| first line | Line_Of_Therapy |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.6 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+ Histological_Type 339 75 114 453 0.82 0.75 0.78
+ Direction 832 163 152 984 0.84 0.85 0.84
+ Staging 229 31 29 258 0.88 0.89 0.88
+ Cancer_Score 37 8 25 62 0.82 0.60 0.69
+ Imaging_Test 2027 214 177 2204 0.90 0.92 0.91
+ Cycle_Number 73 29 24 97 0.72 0.75 0.73
+ Tumor_Finding 1114 64 143 1257 0.95 0.89 0.91
+ Site_Lymph_Node 491 53 60 551 0.90 0.89 0.90
+ Invasion 158 36 23 181 0.81 0.87 0.84
+Response_To_Treatment 431 149 165 596 0.74 0.72 0.73
+ Smoking_Status 66 18 2 68 0.79 0.97 0.87
+ Tumor_Size 1050 112 79 1129 0.90 0.93 0.92
+ Cycle_Count 177 62 53 230 0.74 0.77 0.75
+ Adenopathy 67 12 29 96 0.85 0.70 0.77
+ Age 930 33 19 949 0.97 0.98 0.97
+ Biomarker_Result 1160 169 285 1445 0.87 0.80 0.84
+ Unspecific_Therapy 198 86 80 278 0.70 0.71 0.70
+ Site_Breast 125 15 22 147 0.89 0.85 0.87
+ Chemotherapy 814 55 65 879 0.94 0.93 0.93
+ Targeted_Therapy 195 27 33 228 0.88 0.86 0.87
+ Radiotherapy 276 29 34 310 0.90 0.89 0.90
+ Performance_Status 121 17 14 135 0.88 0.90 0.89
+ Pathology_Test 888 296 162 1050 0.75 0.85 0.79
+ Site_Other_Body_Part 909 275 592 1501 0.77 0.61 0.68
+ Cancer_Surgery 693 119 126 819 0.85 0.85 0.85
+ Line_Of_Therapy 101 11 5 106 0.90 0.95 0.93
+ Pathology_Result 655 279 487 1142 0.70 0.57 0.63
+ Hormonal_Therapy 169 4 16 185 0.98 0.91 0.94
+ Site_Bone 264 81 49 313 0.77 0.84 0.80
+ Biomarker 1259 238 256 1515 0.84 0.83 0.84
+ Immunotherapy 103 47 25 128 0.69 0.80 0.74
+ Cycle_Day 200 36 48 248 0.85 0.81 0.83
+ Frequency 354 27 73 427 0.93 0.83 0.88
+ Route 91 15 22 113 0.86 0.81 0.83
+ Duration 625 161 136 761 0.80 0.82 0.81
+ Death_Entity 34 2 4 38 0.94 0.89 0.92
+ Metastasis 353 18 17 370 0.95 0.95 0.95
+ Site_Liver 189 64 45 234 0.75 0.81 0.78
+ Cancer_Dx 1301 103 93 1394 0.93 0.93 0.93
+ Grade 190 27 46 236 0.88 0.81 0.84
+ Date 807 21 24 831 0.97 0.97 0.97
+ Site_Lung 469 110 90 559 0.81 0.84 0.82
+ Site_Brain 221 64 58 279 0.78 0.79 0.78
+ Relative_Date 1211 401 111 1322 0.75 0.92 0.83
+ Race_Ethnicity 57 8 5 62 0.88 0.92 0.90
+ Gender 1247 17 7 1254 0.99 0.99 0.99
+ Oncogene 345 83 104 449 0.81 0.77 0.79
+ Dosage 900 30 160 1060 0.97 0.85 0.90
+ Radiation_Dose 108 5 18 126 0.96 0.86 0.90
+ macro_avg 24653 3999 4406 29059 0.85 0.84 0.84
+ micro_avg 24653 3999 4406 29059 0.86 0.85 0.85
+```
\ No newline at end of file
diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_posology_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_posology_en.md
new file mode 100644
index 00000000000000..5cf4bb2eb160c1
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_posology_en.md
@@ -0,0 +1,161 @@
+---
+layout: model
+title: Extract Cancer Therapies and Granular Posology Information
+author: John Snow Labs
+name: ner_oncology_posology
+date: 2022-11-24
+tags: [licensed, clinical, en, oncology, ner, treatment, posology]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts cancer therapies (Cancer_Surgery, Radiotherapy and Cancer_Therapy) and posology information at a granular level.
+
+## Predicted Entities
+
+`Cycle_Number`, `Cycle_Count`, `Radiotherapy`, `Cancer_Surgery`, `Cycle_Day`, `Frequency`, `Route`, `Cancer_Therapy`, `Duration`, `Dosage`, `Radiation_Dose`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_posology_en_4.2.2_3.0_1669306988706.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_posology", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_posology", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-----------------|:---------------|
+| adriamycin | Cancer_Therapy |
+| 60 mg/m2 | Dosage |
+| cyclophosphamide | Cancer_Therapy |
+| 600 mg/m2 | Dosage |
+| six courses | Cycle_Count |
+| second cycle | Cycle_Number |
+| chemotherapy | Cancer_Therapy |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_posology|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.3 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+ Cycle_Number 52 4 45 97 0.93 0.54 0.68
+ Cycle_Count 200 63 30 230 0.76 0.87 0.81
+ Radiotherapy 255 16 55 310 0.94 0.82 0.88
+Cancer_Surgery 592 66 227 819 0.90 0.72 0.80
+ Cycle_Day 175 22 73 248 0.89 0.71 0.79
+ Frequency 337 44 90 427 0.88 0.79 0.83
+ Route 53 1 60 113 0.98 0.47 0.63
+Cancer_Therapy 1448 81 250 1698 0.95 0.85 0.90
+ Duration 525 154 236 761 0.77 0.69 0.73
+ Dosage 858 79 202 1060 0.92 0.81 0.86
+Radiation_Dose 86 4 40 126 0.96 0.68 0.80
+ macro_avg 4581 534 1308 5889 0.90 0.72 0.79
+ micro_avg 4581 534 1308 5889 0.90 0.78 0.83
+```
\ No newline at end of file
diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_response_to_treatment_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_response_to_treatment_en.md
new file mode 100644
index 00000000000000..f119de14a2fd47
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_response_to_treatment_en.md
@@ -0,0 +1,147 @@
+---
+layout: model
+title: Extract Mentions of Response to Cancer Treatment
+author: John Snow Labs
+name: ner_oncology_response_to_treatment
+date: 2022-11-24
+tags: [licensed, clinical, en, oncology, ner, treatment]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts entities related to the patient”s response to the oncology treatment, including clinical response and changes in tumor size.
+
+## Predicted Entities
+
+`Response_To_Treatment`, `Size_Trend`, `Line_Of_Therapy`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_response_to_treatment_en_4.2.2_3.0_1669307329775.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_response_to_treatment", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["She completed her first-line therapy, but some months later there was recurrence of the breast cancer. "]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_response_to_treatment", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("She completed her first-line therapy, but some months later there was recurrence of the breast cancer. ").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-----------|:----------------------|
+| first-line | Line_Of_Therapy |
+| recurrence | Response_To_Treatment |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_response_to_treatment|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.4 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+Response_To_Treatment 326 101 157 483 0.76 0.67 0.72
+ Size_Trend 43 28 70 113 0.61 0.38 0.47
+ Line_Of_Therapy 99 11 7 106 0.90 0.93 0.92
+ macro_avg 468 140 234 702 0.76 0.66 0.70
+ micro_avg 468 140 234 702 0.76 0.67 0.71
+```
\ No newline at end of file
diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_test_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_test_en.md
new file mode 100644
index 00000000000000..65fb87ee0d655c
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_test_en.md
@@ -0,0 +1,152 @@
+---
+layout: model
+title: Extract Oncology Tests
+author: John Snow Labs
+name: ner_oncology_test
+date: 2022-11-24
+tags: [licensed, clinical, oncology, en, ner, test]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts mentions of tests from oncology texts, including pathology tests and imaging tests.
+
+## Predicted Entities
+
+`Imaging_Test`, `Biomarker_Result`, `Pathology_Test`, `Biomarker`, `Oncogene`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_test_en_4.2.2_3.0_1669307746859.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_test", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["A biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_test", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("A biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-------------------------------|:---------------|
+| biopsy | Pathology_Test |
+| ultrasound guided thick-needle | Pathology_Test |
+| chest computed tomography | Imaging_Test |
+| CT | Imaging_Test |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_test|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.2 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+ Imaging_Test 2020 229 184 2204 0.90 0.92 0.91
+Biomarker_Result 1177 186 268 1445 0.86 0.81 0.84
+ Pathology_Test 888 276 162 1050 0.76 0.85 0.80
+ Biomarker 1287 254 228 1515 0.84 0.85 0.84
+ Oncogene 365 89 84 449 0.80 0.81 0.81
+ macro_avg 5737 1034 926 6663 0.83 0.85 0.84
+ micro_avg 5737 1034 926 6663 0.85 0.86 0.85
+```
\ No newline at end of file
diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_therapy_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_therapy_en.md
new file mode 100644
index 00000000000000..f60dc17183f344
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_therapy_en.md
@@ -0,0 +1,175 @@
+---
+layout: model
+title: Detect Entities Related to Cancer Therapies
+author: John Snow Labs
+name: ner_oncology_therapy
+date: 2022-11-24
+tags: [clinical, en, licensed, oncology, treatment, ner]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts entities related to oncology therapies using granular labels, including mentions of treatments, posology information and line of therapy.
+
+## Predicted Entities
+
+`Cycle_Number`, `Response_To_Treatment`, `Cycle_Count`, `Unspecific_Therapy`, `Chemotherapy`, `Targeted_Therapy`, `Radiotherapy`, `Cancer_Surgery`, `Line_Of_Therapy`, `Hormonal_Therapy`, `Immunotherapy`, `Cycle_Day`, `Frequency`, `Route`, `Duration`, `Dosage`, `Radiation_Dose`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_therapy_en_4.2.2_3.0_1669308088671.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_therapy", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
+The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
+The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_therapy", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
+The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
+The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-------------------------------|:----------------------|
+| mastectomy | Cancer_Surgery |
+| axillary lymph node dissection | Cancer_Surgery |
+| radiotherapy | Radiotherapy |
+| recurred | Response_To_Treatment |
+| adriamycin | Chemotherapy |
+| 60 mg/m2 | Dosage |
+| cyclophosphamide | Chemotherapy |
+| 600 mg/m2 | Dosage |
+| six courses | Cycle_Count |
+| first line | Line_Of_Therapy |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_therapy|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.4 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+ Cycle_Number 78 41 19 97 0.66 0.80 0.72
+Response_To_Treatment 451 205 145 596 0.69 0.76 0.72
+ Cycle_Count 210 75 20 230 0.74 0.91 0.82
+ Unspecific_Therapy 189 76 89 278 0.71 0.68 0.70
+ Chemotherapy 831 87 48 879 0.91 0.95 0.92
+ Targeted_Therapy 194 28 34 228 0.87 0.85 0.86
+ Radiotherapy 279 35 31 310 0.89 0.90 0.89
+ Cancer_Surgery 720 192 99 819 0.79 0.88 0.83
+ Line_Of_Therapy 95 6 11 106 0.94 0.90 0.92
+ Hormonal_Therapy 170 6 15 185 0.97 0.92 0.94
+ Immunotherapy 96 17 32 128 0.85 0.75 0.80
+ Cycle_Day 205 38 43 248 0.84 0.83 0.84
+ Frequency 363 33 64 427 0.92 0.85 0.88
+ Route 93 6 20 113 0.94 0.82 0.88
+ Duration 527 102 234 761 0.84 0.69 0.76
+ Dosage 959 63 101 1060 0.94 0.90 0.92
+ Radiation_Dose 106 12 20 126 0.90 0.84 0.87
+ macro_avg 5566 1022 1025 6591 0.85 0.84 0.84
+ micro_avg 5566 1022 1025 6591 0.85 0.84 0.84
+```
\ No newline at end of file
diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_tnm_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_tnm_en.md
new file mode 100644
index 00000000000000..3c68a8869d1751
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_tnm_en.md
@@ -0,0 +1,156 @@
+---
+layout: model
+title: Extract Entities Related to TNM Staging
+author: John Snow Labs
+name: ner_oncology_tnm
+date: 2022-11-24
+tags: [licensed, en, clinical, oncology, ner]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts staging information and mentions related to tumors, lymph nodes and metastases.
+
+## Predicted Entities
+
+`Lymph_Node`, `Staging`, `Lymph_Node_Modifier`, `Tumor_Description`, `Tumor`, `Metastasis`, `Cancer_Dx`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_tnm_en_4.2.2_3.0_1669308699155.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_tnm", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_tnm", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-----------------|:------------------|
+| metastatic | Metastasis |
+| breast carcinoma | Cancer_Dx |
+| T2N1M1 stage IV | Staging |
+| 4 cm | Tumor_Description |
+| tumor | Tumor |
+| grade 2 | Tumor_Description |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_tnm|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.2 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+ Lymph_Node 570 77 77 647 0.88 0.88 0.88
+ Staging 232 22 26 258 0.91 0.90 0.91
+Lymph_Node_Modifier 30 5 5 35 0.86 0.86 0.86
+ Tumor_Description 2651 581 490 3141 0.82 0.84 0.83
+ Tumor 1116 72 141 1257 0.94 0.89 0.91
+ Metastasis 358 15 12 370 0.96 0.97 0.96
+ Cancer_Dx 1302 87 92 1394 0.94 0.93 0.94
+ macro_avg 6259 859 843 7102 0.90 0.90 0.90
+ micro_avg 6259 859 843 7102 0.88 0.88 0.88
+```
\ No newline at end of file
diff --git a/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_unspecific_posology_en.md b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_unspecific_posology_en.md
new file mode 100644
index 00000000000000..70b089ad9fc003
--- /dev/null
+++ b/docs/_posts/Ahmetemintek/2022-11-24-ner_oncology_unspecific_posology_en.md
@@ -0,0 +1,152 @@
+---
+layout: model
+title: Extract Cancer Therapies and Posology Information
+author: John Snow Labs
+name: ner_oncology_unspecific_posology
+date: 2022-11-24
+tags: [licensed, clinical, oncology, en, ner, treatment, posology]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts mentions of treatments and posology information using unspecific labels (low granularity).
+
+## Predicted Entities
+
+`Posology_Information`, `Cancer_Therapy`
+
+{:.btn-box}
+Live Demo
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_oncology_unspecific_posology_en_4.2.2_3.0_1669309081671.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = Tokenizer() \
+ .setInputCols(["sentence"]) \
+ .setOutputCol("token")
+
+word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
+ .setInputCols(["sentence", "token"]) \
+ .setOutputCol("embeddings")
+
+ner = MedicalNerModel.pretrained("ner_oncology_unspecific_posology", "en", "clinical/models") \
+ .setInputCols(["sentence", "token", "embeddings"]) \
+ .setOutputCol("ner")
+
+ner_converter = NerConverter() \
+ .setInputCols(["sentence", "token", "ner"]) \
+ .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter])
+
+data = spark.createDataFrame([["The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+ .setInputCol("text")
+ .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
+ .setInputCols("document")
+ .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+ .setInputCols("sentence")
+ .setOutputCol("token")
+
+val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token"))
+ .setOutputCol("embeddings")
+
+val ner = MedicalNerModel.pretrained("ner_oncology_unspecific_posology", "en", "clinical/models")
+ .setInputCols(Array("sentence", "token", "embeddings"))
+ .setOutputCol("ner")
+
+val ner_converter = new NerConverter()
+ .setInputCols(Array("sentence", "token", "ner"))
+ .setOutputCol("ner_chunk")
+
+
+val pipeline = new Pipeline().setStages(Array(document_assembler,
+ sentence_detector,
+ tokenizer,
+ word_embeddings,
+ ner,
+ ner_converter))
+
+val data = Seq("The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+## Results
+
+```bash
+| chunk | ner_label |
+|:-----------------|:---------------------|
+| adriamycin | Cancer_Therapy |
+| 60 mg/m2 | Posology_Information |
+| cyclophosphamide | Cancer_Therapy |
+| 600 mg/m2 | Posology_Information |
+| over six courses | Posology_Information |
+| second cycle | Posology_Information |
+| chemotherapy | Cancer_Therapy |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_oncology_unspecific_posology|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|34.3 MB|
+
+## References
+
+In-house annotated oncology case reports.
+
+## Benchmarking
+
+```bash
+ label tp fp fn total precision recall f1
+Posology_Information 2663 244 399 3062 0.92 0.87 0.89
+ Cancer_Therapy 2580 317 247 2827 0.89 0.91 0.90
+ macro_avg 5243 561 646 5889 0.90 0.89 0.90
+ micro_avg 5243 561 646 5889 0.90 0.89 0.90
+```
\ No newline at end of file